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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Ground-Based Vision Tracker for Advanced Air Mobility and Urban Air Mobility

Advanced Air Mobility (AAM) Air Mobility and Urban Air Mobility (UAM) require aircraft surveillance and monitoring for safety and security. Persistent tracking of flying objects provides Air Traffic Control (ATC) and Air Traffic Management (ATM) continuous coverage and knowledge of the national airspace (NAS). Since there are numerous more AAM and UAM aircraft than commercial aircraft, it will be challenging to utilize the same ATC/ATM architectures. A first step in creating a similar ATC/ATM architecture for AAM/UAM will require ground-based and airborne-based sensors to provide monitoring, which will be difficult in urban environments due to GPS degradation. This paper proposes a vision-based tracking method with static cameras by utilizing image subtraction and blob detection, which avoids adding additional electromagnetic interferences in the environment with sensors such as radar. The ground-based vision tracker (GBVT) outputs the detected objects' azimuth and elevation angles from unmanned aerial system (UAS) flight tests. Future and ongoing work includes sending the detected objects' azimuth and elevation angles as inputs for an extended Kalman filter (EKF) to estimate the position and velocity of the detected object.

distributed sensing↗
NASA NTRSDate not supplied

Ground-Based Vision Tracker for Advanced Air Mobility and Urban Air Mobility

Advanced Air Mobility (AAM) Air Mobility and Urban Air Mobility (UAM) require aircraft surveillance and monitoring for safety and security. Persistent tracking of flying objects provides Air Traffic Control (ATC) and Air Traffic Management (ATM) continuous coverage and knowledge of the national airspace system (NAS). Given the significant disparity in the number of AAM and UAM aircraft compared to commercial aircraft in the NAS, coupled with the dense AAM/UAM operations in urban environments, employing the existing ATC/ATM architectures poses considerable challenges. A first step in creating a similar ATC/ATM architecture for AAM/UAM will require ground-based and airborne-based sensors to provide monitoring, which will be difficult in urban environments due to GPS degradation. This paper proposes a vision-based tracking method with static cameras by utilizing image subtraction and blob detection, which avoids adding additional electromagnetic interferences in the environment with sensors such as radar. The ground-based vision tracker (GBVT) outputs the detected objects' azimuth and elevation angles from unmanned aerial system (UAS) flight tests. Future and ongoing work includes sending the detected objects' azimuth and elevation angles as inputs for an extended Kalman filter (EKF) to estimate the position and velocity of the detected object.

distributed sensing↗
NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Advanced Air Mobility (AAM) encompasses a broad vision for air transportation, including Urban Air Mobility (UAM) as a subset. AAM aims to create a more connected and efficient transportation network across various geographical settings. However, navigating AAM aircraft in GPS-denied or degraded environments during approach and landing is challenging. Traditional vision aids like glideslopes and localizers are limited in vertiport environments due to narrow beam constraints and reduced landing angle options. This paper addresses the need for accurate navigation solutions at vertiports by proposing a vision-based distributed sensing (VIDIS) system utilizing cameras with bundle adjustment to assist incoming AAM aircraft during approach and landing while monitoring surface movements to enhance safety and efficiency. Key focus areas for current and future vertiport developers include identifying suitable sensor types and infrastructure standards to support AAM operations and including vertiport markings as vision-based navigation aids. The proposed system offers a novel approach to overcoming navigation challenges in AAM operations, particularly in urban settings where traditional aids may be insufficient. Preliminary simulation results with distributed cameras demonstrate promising outcomes for implementing bundle adjustment techniques to enhance vision-based navigation solutions at vertiports. Generating waypoint-based trajectories via waypoint integration using explicit guidance synthesis (WINGS) creates smooth AAM trajectories for landing at vertiports by using the current waypoint's terminal conditions as the initial conditions for the next waypoint. Combining bundle adjustment's ground-based solution of vertiport features with WINGS, Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT), and an extended Kalman filter (EKF) estimates the state of an incoming aircraft during approach and landing at vertiports. Future work includes testing VIDIS in a high-fidelity simulation and with real-world data.

Distributed sensing↗
NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Advanced Air Mobility (AAM) encompasses a broad vision for air transportation, including Urban Air Mobility (UAM) as a subset. AAM aims to create a more connected and efficient transportation network across various geographical settings. However, navigating AAM aircraft in GPS-denied or degraded environments during approach and landing is challenging. Traditional vision aids like glideslopes and localizers are limited in vertiport environments due to narrow beam constraints and reduced landing angle options. This paper addresses the need for accurate navigation solutions at vertiports by proposing a vision-based distributed sensing (VIDIS) system utilizing cameras with bundle adjustment to assist incoming AAM aircraft during approach and landing while monitoring surface movements to enhance safety and efficiency. Key focus areas for current and future vertiport developers include identifying suitable sensor types and infrastructure standards to support AAM operations and including vertiport markings as vision-based navigation aids. The proposed system offers a novel approach to overcoming navigation challenges in AAM operations, particularly in urban settings where traditional aids may be insufficient. Preliminary simulation results with distributed cameras demonstrate promising outcomes for implementing bundle adjustment techniques to enhance vision-based navigation solutions at vertiports. Generating waypoint-based trajectories via waypoint integration using explicit guidance synthesis (WINGS) creates smooth AAM trajectories for landing at vertiports by using the current waypoint's terminal conditions as the initial conditions for the next waypoint. Combining bundle adjustment's ground-based solution of vertiport features with WINGS, Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT), and an extended Kalman filter (EKF) estimates the state of an incoming aircraft during approach and landing at vertiports. Future work includes testing VIDIS in a high-fidelity simulation and with real-world data.

Distributed sensing↗
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learningautonomous systems; flight simulat↗
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learning↗
NASA NTRSDate not supplied

Preliminary Evaluation of National Campaign Scenarios for Urban Air Mobility

Urban Air Mobility and Advanced Air Mobility concepts offer a novel method for transportation of passengers and cargo. Whereas the concept may reduce congestion of roads and highways, it also introduces new complexity to the National Airspace in terms of management of such operations. NASA, in partnership with FAA and industry, approaches these complexities with research and development activities such as flight tests and simulations, in order to better understand the impacts and necessary mechanisms by which these operations could be integrated. One example of such research and development activity that NASA is conducting is the Advanced Air Mobility National Campaign effort. Flight-test scenarios were proposed as part of the National Campaign and tested in simulation for evaluation with industry partners prior to the Flight test. This simulation, exercised as an engineering evaluation, provided a data collection opportunity to better understand what iterations and modifications would be needed to successfully demonstrate the flight-test scenarios in order to mature the concept of Urban Air Mobility and Advanced Air Mobility.

urban air mobility↗
NASA NTRSDate not supplied

Preliminary Evaluation of National Campaign Scenarios for Urban Air Mobility

Urban Air Mobility and Advanced Air Mobility concepts offer a novel method for transportation of passengers and cargo. Whereas the concept may reduce congestion of roads and highways, it also introduces new complexity to the National Airspace in terms of management of such operations. NASA, in partnership with FAA and industry, approaches these complexities with research and development activities such as flight tests and simulations, in order to better understand the impacts and necessary mechanisms by which these operations could be integrated. One example of such research and development activity that NASA is conducting is the Advanced Air Mobility National Campaign effort. Flight-test scenarios were proposed as part of the National Campaign and tested in simulation for evaluation with industry partners prior to the Flight test. This simulation, exercised as an engineering evaluation, provided a data collection opportunity to better understand what iterations and modifications would be needed to successfully demonstrate the flight-test scenarios in order to mature the concept of Urban Air Mobility and Advanced Air Mobility.

Urban Air Mobility↗
NASA NTRS2021

Advanced Air Mobility(AAM) Ecosystems Working Group Briefing Urban Air Mobility Noise Working Group (UNWG) Subgroup 2: Ground & Flight Testing

Overview of Urban Air Mobility Noise Working Group (UNWG) activities of Subgroup 2, responsible for developing Urban Air Mobility (UAM) vehicle ground and flight testing recommendations for those tasked with evaluating the acoustic characteristics of UAM vehicles under development. This topic is being briefed to an audience at the Advanced Air Mobility (AAM) Ecosystems Working Group Meeting (AEWG).

acoustics↗
NASA NTRS2019

Exploration of Near-Term Potential Routes and Procedures for Urban Air Mobility

Urban air mobility is gaining interest as the need for On Demand Mobility in today's congested traffic is becoming high in metropolitan areas. Urban Air Mobility (UAM) is envisioned as a concept to transport passengers and cargo safely and efficiently using innovative aircraft in the urban areas. It is expected to improve mobility for the general public, decongest road traffic, reduce transport time and reduce the strain on existing public transport networks. There exist several challenges to Urban Air Mobility (UAM) such as integration of procedures with airspace and the airport, noise levels that are acceptable to the general public, public safety, public acceptance, vehicle certification, and more. Most of the research in the United States and European skies (DLR - German Aerospace Center) related to urban areas has focused on small UAS (Unmanned Aircraft Systems) flights (NASA's UTM (UAS Traffic Management) research) and their integration with the airspace and building safe operations in densely populated areas. Previous studies on UAM have focused on fast time simulations of the routes that are separated via a separation service and network of routes. Similarly, research in Europe has focused on the approach profile for these innovative aircraft, vertiports and battery life among others. UAM as a part of the On-Demand Mobility effort has provided some guidelines for operations as shown below: Does not require additional ATC (Air Traffic Control) infrastructure; Does not impose additional workload on ATC; Does not restrict operations of traditional airspace users; Will meet appropriate safety thresholds and requirements; Will prioritize operational scalability; Will allow flexibility where possible and structure where necessary. This paper explores potential routes and procedures in a Human-In-The-Loop (HITL) experiment that could be applied in the near-term to allow integration of UAM flights into the airspace as well as a large airport. The airspace that was explored was Dallas Fort Worth (DFW) airspace managed by the DFW East Tower in South Flow only. In addition, Dallas Love Field (DAL) and Addison (ADS) airspace were also part of the testbed. The initial set of routes investigated in this study were published helicopter routes in the DFW area. Figure 1 shows class B airspace in DFW area and the origin/destination city pairs where UAM flights flew along with helicopter routes shown in blue. The research focused on exploring procedures for integrating UAM flights into Class Bravo and Class Delta airspace. Three different communication procedures, evaluated with three different levels of UAM traffic, are shown in Table 1. The current day routes were evaluated with current day communication procedures were explored as the first condition. The current day routes were also evaluated in the second condition with reduced communications, which was assumed due to the presence of a Letter Of Agreement (LOA). The purpose of the LOA was to reduce the verbiage associated with pilots getting clearance to Class B airspace from the controllers, pre-assigning beacons codes to the UAM flights, separate routes by assigning altitudes and speeds to flights going in any one direction. Flights were expected to automatically change frequency when exiting Class B airspace, thus transition points for entry and exit points were also specified in the LOA.

Urban Air Mobility↗
NASA NTRS2021

Decentralized Control Synthesis for Air Traffic Management in Urban Air Mobility

Urban air mobility (UAM) refers to air transportation services within an urban area, often in an on-demand fashion. We study air traffic management (ATM) for vehicles in a UAM fleet, while guaranteeing system safety requirements such as traffic separation. Existing ATM methods for unmanned aerial systems, such as UAS traffic management, utilize alternative approaches which do not provide strict safety guarantees. No established infrastructure exists for providing ATM at scale for UAM. We provide a decentralized, hierarchical approach for UAM ATM that allows for scalability to high traffic densities as well as providing theoretical guarantees of correctness with respect to user-provided safety specifications. Our main contributions are two-fold. First, we propose a novel UAM ATM architecture that divides the control authority between vertihubs that are each in charge of all UAM vehicles in their local airspace. Each vertihub also contains a number of vertiports that are in charge of UAM vehicle takeoffs and landings. The resulting architecture is decentralized and hierarchical, which not only enables scalability, but also robustness in the event of any individual vertihub or vertiport no longer being operational. Second, we provide a contract-based correct-by-construction reactive synthesis approach that provably guarantees safety properties with respect to user-provided specifications in linear temporal logic. We demonstrate the approach on large-volume UAM air traffic data.

Urban Air Mobility↗
NASA NTRSDate not supplied

Evaluation of Initial and Mid-Term Air Traffic Procedures for Urban Air Mobility

Urban air mobility (UAM) operations are expected to expand in scale over the next several years as novel aircraft types, including electric vertical takeoff and landing aircraft, are certified and begin operations. These new aircraft may increase safety, decrease noise, and lower operating costs compared with helicopters, allowing them to operate in ways existing aircraft do not. It is vital that these expanded operations are compatible with and do not disrupt existing operations or the air traffic management system. To study the ways in which scaled UAM operations can best integrate in the national airspace system, NASA and Joby Aviation partnered to conduct a high-fidelity air traffic controller-in-the-loop study. Building on air traffic procedures used to manage high tempo operations in other parts of the airspace, new procedures, routes, and communications protocols were developed and tested by retired controllers in NASA’s Future Flight Central tower simulation facility. In addition, new cooperative airspace constructs in the form of corridors were developed to understand their potential contributions to even greater scales of operation. The controllers managed traffic scenarios in the Dallas-Fort Worth and Dallas Love Field airports consisting of fleets of up to 100 UAM aircraft operating alongside traditional traffic. Metrics for air traffic controller workload, duration of communications, departure delays, and other measures of allowable aircraft throughput were collected. The analysis indicates that using today’s procedures for initial UAM operations under nominal conditions could enable up to 40 operations per hour to an airport’s central terminal area if that involved crossing a runway and up to 55 operations per hour if reaching the central terminal did not involve crossing a runway. Operations at these tempos did not delay or otherwise interfere with simulated runway traffic and were rated acceptable by the air traffic controllers. The new corridor constructs dramatically lowered controller workload in certain circumstances, suggesting they may be effective in further increasing the allowable scale of operations.

Advanced Air Mobility↗
NASA NTRSDate not supplied

Operational Analysis to Evaluate Practical Potential of Vertical Vertiplex for Urban Air Mobility

Urban Air Mobility (UAM) is intended to serve as an alternative mode of transportation to relieve congestions in and out of urban areas. Therefore, a high density vertiplex (HDV) with multiple touchdowns and liftoff (TLOF) zone is necessary to alleviate the demands. To increase the operational efficiency of the vertiplex, EVTOL aircraft are distributed to parking spaces to utilize the TLOF zone for another approach and departure procedure. The surface footprint of the infrastructure increases drastically due to the horizontal arrangement of taxiways and parking spaces. Because high-demand locations will likely be in an urban environment with space constraints, consideration to decrease the surface footprint of the infrastructure without jeopardizing the operational efficiency is necessary for the realization of UAM. The first part of this paper proposes an innovative approach to vertically orient taxiways and parking spaces by utilizing a vertical lift to transport EVTOL aircraft within the vertiplex while meeting the safety-critical requirements outlined in Heliport Design Advisory Circular: AC 150/5390-2c. The second part of the paper evaluates the practical potential of the proposed vertical vertiplex by analyzing relative surface area utilization and operational capacity.

Smart Air Mobility↗
NASA NTRSDate not supplied

Minimum-Violation Traffic Management for Urban Air Mobility

Urban air mobility (UAM) refers to air transportation services in and over an urban area and has the potential to revolutionize mobility solutions. However, due to the projected scale of operations, current air traffic management (ATM) techniques are not viable. Increasingly autonomous systems are a pathway to accelerate the realization of UAM operations but must be fielded safely and efficiently. The heavily regulated, safety critical nature of aviation may lead to multiple, competing safety constraints that can be traded off based on the operational context. In this paper, we design a framework which allows for the scalable planning of a UAM ATM system. We formalize safety oriented constraints derived from FAA regulations by encoding them as temporal logic formulae. We then propose a method for UAM ATM that is both scalable and minimally violates the temporal logic constraints. Numerical results show that the runtime for our proposed algorithm is suitable for very large problems and is backed by theoretical guarantees of correctness with respect to given temporal logic constraints.

Urban Air Mobility↗
NASA NTRSDate not supplied

Assured Contingency Landing Management for Advanced Air Mobility

Advanced Air Mobility (AAM) is quickly developing as a new air transportation system that moves people and packages in the regions previously not / less served by the current aviation systems. Such AAM must operate safely despite the potential to encounter hazards and experience anomalies and failures in-flight. It becomes especially important to have systematic auto-mitigation strategies to perform safe contingency actions in AAM flight operations, as pilots have limited Situational Awareness (SA) and limited time to make prompt decisions when encountering failures/anomalies in high-density low altitude airspace. This paper presents Assured Contingency Landing Management (ACLM) with an online landing strategy selection to decide between the following three options when a contingency landing is required: (1) Return-to-launch landing site, (2) Land immediately at a nearby clear but unprepared site, (3) Land at a prepared landing site from the approximate footprint. Our presented algorithm shows a real-time auto-mitigation loop with multiple threads that run simultaneously to check controllability, reachability, and intermediate decisions to hold/ loiter or continue the flight plan as the landing strategy solution is being computed. Case study simulation is demonstrated with the safety-critical propulsion system and battery system and shows how different failure scenarios impact the landing strategy selection.

Autonomous Mitigation↗